Applied AI ML Executive Director, Chief Data & Analytics Office

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 210,000 - 320,000; Full time

Job summary

JPMorgan Chase & Co. is seeking an Applied AI ML Executive Director within the Chief Data and Analytics Office in Jersey City to lead the design, build, and scale of Generative AI capabilities across the enterprise.

You will guide production systems-spanning model development, deployment, and continuous improvement while creating reusable services to accelerate adoption. You will partner with senior stakeholders to prioritize use cases, measure impact, and drive transformation at scale,

Qualifications

• PhD/MS in Computer Science or related field with extensive AI/ML experience.
• Strong track record deploying AI/ML into production at scale with reliability and observability.
• Expertise in distributed systems, model training, serving, and lifecycle management.
• Proven ability to influence stakeholders and lead cross-functional teams.

Responsibilities

• Architect end-to-end Generative AI and agentic AI solutions for complex workflows.
• Lead multi-agent systems to orchestrate tasks and scale end-to-end processes.
• Translate business goals into AI/ML platform capabilities with reliability and security.
• Build reusable AI/ML frameworks and services for enterprise adoption.
• Establish production engineering rigor, including observability and incident readiness.
• Mentor a high-performing AI engineering team and drive governance for experiments.

Skills

Distributed computing patterns

AI production deployment

Stakeholder management

Team leadership

Experiment design

Education

PhD in Computer Science or related field

MS in Computer Science or related field

Tools

AWS SageMaker

AWS Bedrock

Monitoring and observability tools

Job description

Join a world-class data science team at JPMorgan Chase and help shape the future of our Chief Administrative Office. As a leader in applied AI and machine learning,you’llhave the opportunity to work on high-impact projects that influence the way we do business across multiple domains. Collaborate with talented colleagues,leveragecutting-edgetechnologies, and see your work make a tangible difference. We value curiosity, technical excellence, and a passion for solving complex problems. Ifyou’reready to accelerate your career and drive meaningful change, we want to hear from you.

As an Applied AI ML Executive Director in the Chief Data and Analytics Office, you will lead the design, build, and scale of Generative AI and agentic AI capabilities that solve complex operational challenges. You will guide a team that delivers production systems-spanning model development, software engineering, deployment, and continuous improvement-while building reusable services that accelerate adoption across teams. You will partner closely with senior stakeholders to prioritize use cases, measure impact, and drive enterprise-scale transformation.

Job responsibilities

• Architect end-to-end Generative AI and agentic AI solutions that automate complex operational workflows with measurable business outcomes
• Lead the design and delivery of multi-agent systems that decompose complex problems, orchestrate tasks, and reliably execute end-to-end workflows at scale
• Translate business objectives into robust AI/ML product and platform capabilities, balancing speed of delivery with reliability, security, and long-term maintainability
• Build reusable frameworks, libraries, and services that enable other AI teams to standardize patterns for model development, evaluation, deployment, and monitoring
• Establish production engineering rigor across AI/ML delivery, including observability, performance tuning, incident readiness, and operational runbooks
• Partner with cross-functional stakeholders to identify high-value opportunities, define success metrics, and scale solutions through adoption and change management
• Mentor and develop a high-performing team of AI engineers and researchers, creating a culture of technical excellence, experimentation, and continuous learning
• Drive governance for experimentation and iteration, ensuring feedback loops and evaluation practices improve model and agent behavior over time

Required qualifications, capabilities, and skills

• PhD in Computer Science or a related quantitative discipline with 8+ years of relevant experience, or MS in Computer Science (or related field) with 12+ years of relevant experience
• Formal training or certification in applied AI and machine learning concepts
• Proven track record of deploying AI/ML applications into production environments at scale, including reliability, monitoring, and lifecycle management
• Strong understanding of AI/ML fundamentals, including experimental design, evaluation methods, and data analysis techniques
• Experience with distributed computing patterns for model training, model serving, and state persistence in production systems
• Demonstrated ability to design systems that incorporate user feedback loops to refine agent behavior and improve performance over time
• Demonstrated experience building, mentoring, and leading high-performing AI/ML teams delivering complex outcomes with cross-functional partners
• Strong communication and stakeholder management skills, including the ability to influence prioritization and align delivery to business value

Preferred qualifications, capabilities, and skills

• Experience deploying and operating models on Amazon Web Services platforms, including Amazon SageMaker and/or Amazon Bedrock
• Experience building agentic or multi-agent systems, including orchestration patterns, tool-use design, and guardrails for safe and reliable execution
• Experience establishing evaluation strategies for Generative AI systems (for example, quality scoring, test sets, and human-in-the-loop review)
• Experience building reusable AI/ML platforms or shared services adopted by multiple teams across an enterprise
• Familiarity with modern MLOps and LLMOps practices, including automated deployment, monitoring, and continuous improvement workflows

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